<div>Teachable Machine Pose Model</div>
<button type='button' onclick='init()'>Start</button>
<div><canvas id='canvas'></canvas></div>
<div id='label-container'></div>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.3.1/dist/tf.min.js"></script>
<script src="../dist/teachablemachine-pose.min.js"></script>
<script type="text/javascript">
    // More API functions here:
    // https://github.com/googlecreativelab/teachablemachine-community/tree/master/libraries/pose

    const URL = 'https://teachablemachine.withgoogle.com/models/M4cegN5A/';

    let model, webcam, ctx, labelContainer, maxPredictions;

    async function init() {
        const modelURL = URL + 'model.json';
        const metadataURL = URL + 'metadata.json';

        // load the model and metadata
        // Refer to tmImage.loadFromFiles() in the API to support files from a file picker
        model = await tmPose.load(modelURL, metadataURL);
        maxPredictions = model.getTotalClasses();

        // Convenience function to setup a webcam
        const size = 800;
        const flip = true; // whether to flip the webcam
        webcam = new tmPose.Webcam(size, size, flip); // width, height, flip
        await webcam.setup(); // request access to the webcam
        webcam.play();
        window.requestAnimationFrame(loop);

        // append/get elements to the DOM
        const canvas = document.getElementById('canvas');
        canvas.width = size; canvas.height = size;
        ctx = canvas.getContext('2d');
        labelContainer = document.getElementById('label-container');
        for (let i = 0; i < maxPredictions; i++) { // and class labels
            labelContainer.appendChild(document.createElement('div'));
        }
    }

    async function loop(timestamp) {
        webcam.update(); // update the webcam frame
        await predict();
        window.requestAnimationFrame(loop);
    }

    async function predict() {
        // Prediction #1: run input through posenet
        // estimatePose can take in an image, video or canvas html element
        const { pose, posenetOutput } = await model.estimatePose(webcam.canvas);
        // Prediction 2: run input through teachable machine classification model
        const prediction = await model.predict(posenetOutput);

        for (let i = 0; i < maxPredictions; i++) {
            const classPrediction =
                prediction[i].className + ': ' + prediction[i].probability.toFixed(2);
            labelContainer.childNodes[i].innerHTML = classPrediction;
        }

        // finally draw the poses
        drawPose(pose);
    }

    function drawPose(pose) {
        if (webcam.canvas) {
            ctx.drawImage(webcam.canvas, 0, 0);
            // draw the keypoints and skeleton
            if (pose) {
                const minPartConfidence = 0.5;
                tmPose.drawKeypoints(pose.keypoints, minPartConfidence, ctx);
                tmPose.drawSkeleton(pose.keypoints, minPartConfidence, ctx);
            }
        } 
    }
</script>